The current paper contributes to the concept of activity-related biometric authentication in ambient Intelligence environments. The motivation behind the proposed approach derives from activity-related biometrics and is mainly focusing on everyday activities. The activity sequence is captured by a stereoscopic camera and the resulting 2.5D data are processed to extract valuable unobtrusive activity-related features. The novel contribution of the current work lies in the warping of the extracted movements trajectories, so as to compensate for different environmental settings. Au- thentication is performed utilizing both HMM and GMMs. The authentication results performed on a database with 32 subjects show that the current work outperforms existing approaches especially in the case of non-interaction restricting scenarios.